Since the efficiency of rice husk biomass power plants in Thailand has never been reliably assessed, this study undertook the task of measuring the technical efficiency of Very Small Power Productions (VSPPs). The secondary data recorded in 2012 were collected from the power policy bureau in Thailand. Two concepts of Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) were adopted to analyze a group of 57 biomass power plants.The results indicated that a SFA model exhibited the highest score of 0.877, followed by a Constant Return to Scale-DEA (CRS-DEA) and Variable Return to Scale-DEA (VRS-DEA) at 0.841 and 0.722, respectively. Input surpluses of capacity and rice husks were highlighted to improve unit efficiency. Moreover, we found that the efficiency scores derived from the VRS-DEA and SFA models were more consistent than those computed by the CRS-DEA and SFA models. Accomplishing the Thai government's goal of sustainable, renewable energy will encourage more utility plants to use rice husk for electricity generation.
Aim: This research aims to study the socio-economic, psychological, and environmental factors which affect the decision making of passengers with regard to the use of public transportation route from Nong Khai province, which is considered as the border area of Thailand and Laos, to many destinations. Methods: The study was initially carried out by collecting data from 450 passengers using two public transportation services and 200 passengers using private cars. An analysis was conducted by means of descriptive statistics, factor analysis and binary logistic regression. Results: The results showed that the status, age, domicile, self-satisfaction and accomplishment, safety of life and property, and self-consciousness and environmental conservation practice influenced the choice of both the alternatives. Conclusion: To develop a service model, a variety of bus and van services must be provided to match the varied demand of passengers with different levels of purchasing power. It is also important to consider the impact of various factors that affect the public bus service selection, which may result in improved public transport systems. As a consequence, the well-being of border area citizens can be improved.
The data, focusing on the supply of pineapple for industrial processing in Thailand during the period from 2011-2020, was acquired. The data indicated that productivity had tended to decrease during the period between 2011-2015 due to phenomena of drought and a reduction in prices, while increasing trends were observed during the years between 2016 - 2019. In the year 2020, Thailand was the biggest exporter of canned pineapple in the world, and the export value was approximately 345 million U.S. dollars. During the process, the generated by-products were peels, cores, stems, and crowns at approximately 35.5, 14.7, 4.6, and 4.3%, respectively. Based on the annual production of 1,689,884 tons, the total by-products from pineapple processing would generate 993,402.4 tons, which could be divided into peels, cores, stems, and crowns at 596,713.8, 247,089.9, 773,20.7, and 722,78.0 tons, respectively. Valorization of by-product for health applications such as pharmaceutical, cosmetic, and health food has been reviewed.
The energy business has played an important role in an economic growth of Taiwan because the market share is in the high value that can make a significant contribution towards regional and local employment. However, Taiwan is lack of energy resources, making the country highly relies on an import for more than 98 percent of its all energy. At present, a top priority of the countrys policy is to develop clean, sustainable, independent, and efficient energy in order to eliminate the vulnerability from external disruption. Therefore, this research aims to assess the operating efficiency and to analyze factors affecting the efficiency scores of the registered energy companies in the Taiwan Stock Exchange (TWSE) recorded during 2003-2012. The super-efficiency data envelopment analysis (SE-DEA) was initially applied to reveal the additional efficiency scores, followed by the Tobit regression model used to analyze what factors determine the efficiency scores. The empirical results showed that seven DMUs performed efficiently, ranking from 7.29 to 1.02. The company with the best operating performance was Taiwan Cogeneration Corporation (TCC), while the Great Taipei Gas Corporation (GTG) revealed the worst efficiency score. Furthermore, the Tobit regression model explained that the higher number of the local employees, the greater the efficiency scores were. Besides, the lower number of the shareholders, the greater the efficiency scores were. As a result, the Taiwans government is supposed to encourage all energy companies to have a higher number of local employees and shareholders to increase their efficiency scores.
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